class_docstr: Compute node embeddings for incoming and outgoing directions
  separately, and then concatenate the two output node embeddings
  after the final layer.
  .. math::
     h_{i}^{0} = [ x_i \| \mathbf{0} ]
  
     a_{i, \vdash}^{t} = \sum_{j\in\mathcal{N}_{\vdash }(i)} W_{\vdash} h_{j, \vdash}^{t}
  
     a_{i, \dashv}^{t} = \sum_{j\in\mathcal{N}_{\dashv }(i)} W_{\dashv} h_{j, \dashv}^{t}
  
     h_{i, \vdash}^{t+1} = \mathrm{GRU}_{\vdash}(a_{i, \vdash}^{t}, h_{i, \vdash}^{t})
  
     h_{i, \dashv}^{t+1} = \mathrm{GRU}_{\dashv}(a_{i, \dashv}^{t}, h_{i, \dashv}^{t})
  Attributes
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
  Attributes
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
code: def __init__(self, input_size, output_size, n_etypes=1, bias=True):
      super(BiSepGGNNLayerConv, self).__init__()
      self._input_size = input_size
      self._output_size = output_size
      # self._num_layers = num_layers
      self._n_etypes = n_etypes
  
      self.linears_in = nn.ModuleList(
          [nn.Linear(output_size, output_size) for _ in range(n_etypes)]
      )
  
      self.linears_out = nn.ModuleList(
          [nn.Linear(output_size, output_size) for _ in range(n_etypes)]
      )
  
      # self.update_in = nn.Linear(input_size + output_size, output_size, bias=True)
      # self.update_out = nn.Linear(input_size + output_size, output_size, bias=True)
  
      self.gru_in = nn.GRUCell(output_size, output_size, bias=bias)
      self.gru_out = nn.GRUCell(output_size, output_size, bias=bias)
      self.reset_parameters()
  
imports: import dgl
  import dgl.function
  import torch
  import torch.nn
  from torch.nn import init
  from ...data.data import GraphData
  from .base import GNNBase, GNNLayerBase
label_desc: Use image node_id 1 to create a new BiSepGGNNLayerConv object from inherited base classes: GNNLayerBase with example: obj = BiSepGGNNLayerConv(input_size, output_size, n_etypes, bias)